Modelling Oil Price with Lie Algebras and Long Short-Term Memory Networks

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Bildirici M. E., Güler Bayazıt N., Uçan Y.

MATHEMATICS, vol.9, no.14, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 14
  • Publication Date: 2021
  • Doi Number: 10.3390/math9141708
  • Journal Name: MATHEMATICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Metadex, zbMATH, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: oil price forecasting, Lie group SO(3), LSTM, deep learning, short-term model, CHAOS
  • Yıldız Technical University Affiliated: Yes


In this paper, we propose hybrid models for modelling the daily oil price during the period from 2 January 1986 to 5 April 2021. The models on S2 manifolds that we consider, including the reference ones, employ matrix representations rather than differential operator representations of Lie algebras. Firstly, the performance of Lie(NLS) model is examined in comparison to the Lie-OLS model. Then, both of these reference models are improved by integrating them with a recurrent neural network model used in deep learning. Thirdly, the forecasting performance of these two proposed hybrid models on the S2 manifold, namely Lie-LSTMOLS and Lie-LSTMNLS, are compared with those of the reference Lie(OLS) and Lie(NLS) models. The in-sample and out-of-sample results show that our proposed methods can achieve improved performance over Lie(OLS) and Lie(NLS) models in terms of RMSE and MAE metrics and hence can be more reliably used to assess volatility of time-series data.